
import re
import string
from collections import Counter

import sacrebleu
import sacremoses
from packaging import version

import datasets


__lowerCAmelCase     :	Dict       			=  '\n@inproceedings{xu-etal-2016-optimizing,\n    title = {Optimizing Statistical Machine Translation for Text Simplification},\n    authors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},\n    journal = {Transactions of the Association for Computational Linguistics},\n    volume = {4},\n    year={2016},\n    url = {https://www.aclweb.org/anthology/Q16-1029},\n    pages = {401--415\n},\n@inproceedings{post-2018-call,\n    title = "A Call for Clarity in Reporting {BLEU} Scores",\n    author = "Post, Matt",\n    booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",\n    month = oct,\n    year = "2018",\n    address = "Belgium, Brussels",\n    publisher = "Association for Computational Linguistics",\n    url = "https://www.aclweb.org/anthology/W18-6319",\n    pages = "186--191",\n}\n'

__lowerCAmelCase     :	Optional[Any]       			=  '\\nWIKI_SPLIT is the combination of three metrics SARI, EXACT and SACREBLEU\nIt can be used to evaluate the quality of machine-generated texts.\n'


__lowerCAmelCase     :	Optional[int]       			=  '\nCalculates sari score (between 0 and 100) given a list of source and predicted\nsentences, and a list of lists of reference sentences. It also computes the BLEU score as well as the exact match score.\nArgs:\n    sources: list of source sentences where each sentence should be a string.\n    predictions: list of predicted sentences where each sentence should be a string.\n    references: list of lists of reference sentences where each sentence should be a string.\nReturns:\n    sari: sari score\n    sacrebleu: sacrebleu score\n    exact: exact score\n\nExamples:\n    >>> sources=["About 95 species are currently accepted ."]\n    >>> predictions=["About 95 you now get in ."]\n    >>> references=[["About 95 species are currently known ."]]\n    >>> wiki_split = datasets.load_metric("wiki_split")\n    >>> results = wiki_split.compute(sources=sources, predictions=predictions, references=references)\n    >>> print(results)\n    {\'sari\': 21.805555555555557, \'sacrebleu\': 14.535768424205482, \'exact\': 0.0}\n'

def a__       (			A_   ):






  '''simple docstring'''





  def remove_articles(A_   ):
    __magic_name__								=			re.compile(R"""\b(a|an|the)\b""",	re.UNICODE   )
    return re.sub(A_,	""" """,	A_   )

  def white_space_fix(A_   ):
    return " ".join(text.split()   )

  def remove_punc(A_   ):
    __magic_name__								=			set(string.punctuation   )
    return "".join(ch for ch in text if ch not in exclude   )

  def lower(A_   ):
    return text.lower()

  return white_space_fix(remove_articles(remove_punc(lower(A_   )   )   )   )

def a__       (			A_,	A_   ):






  '''simple docstring'''





  return int(normalize_answer(A_   ) == normalize_answer(A_   )   )

def a__       (			A_,	A_   ):






  '''simple docstring'''





  __magic_name__								=			[any(compute_exact(A_,	A_   ) for ref in refs   ) for pred, refs in zip(A_,	A_   )]
  return (sum(A_   ) / len(A_   )) * 100

def a__       (			A_,	A_,	A_,	A_   ):






  '''simple docstring'''





  __magic_name__								=			[rgram for rgrams in rgramslist for rgram in rgrams]
  __magic_name__								=			Counter(A_   )

  __magic_name__								=			Counter(A_   )
  __magic_name__								=			Counter()
  for sgram, scount in sgramcounter.items():
    __magic_name__								=			scount * numref

  __magic_name__								=			Counter(A_   )
  __magic_name__								=			Counter()
  for cgram, ccount in cgramcounter.items():
    __magic_name__								=			ccount * numref

  # KEEP
  __magic_name__								=			sgramcounter_rep & cgramcounter_rep
  __magic_name__								=			keepgramcounter_rep & rgramcounter
  __magic_name__								=			sgramcounter_rep & rgramcounter

  __magic_name__								=			0
  __magic_name__								=			0
  for keepgram in keepgramcountergood_rep:
    keeptmpscorea += keepgramcountergood_rep[keepgram] / keepgramcounter_rep[keepgram]
    # Fix an alleged bug [2] in the keep score computation.
    # keeptmpscore2 += keepgramcountergood_rep[keepgram] / keepgramcounterall_rep[keepgram]
    keeptmpscorea += keepgramcountergood_rep[keepgram]
  # Define 0/0=1 instead of 0 to give higher scores for predictions that match
  #      a target exactly.
  __magic_name__								=			1
  __magic_name__								=			1
  if len(A_   ) > 0:
    __magic_name__								=			keeptmpscorea / len(A_   )
  if len(A_   ) > 0:
    # Fix an alleged bug [2] in the keep score computation.
    # keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
    __magic_name__								=			keeptmpscorea / sum(keepgramcounterall_rep.values()   )
  __magic_name__								=			0
  if keepscore_precision > 0 or keepscore_recall > 0:
    __magic_name__								=			2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)

  # DELETION
  __magic_name__								=			sgramcounter_rep - cgramcounter_rep
  __magic_name__								=			delgramcounter_rep - rgramcounter
  __magic_name__								=			sgramcounter_rep - rgramcounter
  __magic_name__								=			0
  __magic_name__								=			0
  for delgram in delgramcountergood_rep:
    deltmpscorea += delgramcountergood_rep[delgram] / delgramcounter_rep[delgram]
    deltmpscorea += delgramcountergood_rep[delgram] / delgramcounterall_rep[delgram]
  # Define 0/0=1 instead of 0 to give higher scores for predictions that match
  # a target exactly.
  __magic_name__								=			1
  if len(A_   ) > 0:
    __magic_name__								=			deltmpscorea / len(A_   )

  # ADDITION
  __magic_name__								=			set(A_   ) - set(A_   )
  __magic_name__								=			set(A_   ) & set(A_   )
  __magic_name__								=			set(A_   ) - set(A_   )

  __magic_name__								=			0
  for addgram in addgramcountergood:
    addtmpscore += 1

  # Define 0/0=1 instead of 0 to give higher scores for predictions that match
  # a target exactly.
  __magic_name__								=			1
  __magic_name__								=			1
  if len(A_   ) > 0:
    __magic_name__								=			addtmpscore / len(A_   )
  if len(A_   ) > 0:
    __magic_name__								=			addtmpscore / len(A_   )
  __magic_name__								=			0
  if addscore_precision > 0 or addscore_recall > 0:
    __magic_name__								=			2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)

  return (keepscore, delscore_precision, addscore)

def a__       (			A_,	A_,	A_   ):






  '''simple docstring'''





  __magic_name__								=			len(A_   )

  __magic_name__								=			ssent.split(""" """   )
  __magic_name__								=			csent.split(""" """   )
  __magic_name__								=			[]
  __magic_name__								=			[]
  __magic_name__								=			[]
  __magic_name__								=			[]
  __magic_name__								=			[]
  __magic_name__								=			[]

  __magic_name__								=			[]
  __magic_name__								=			[]
  __magic_name__								=			[]
  __magic_name__								=			[]
  for rsent in rsents:
    __magic_name__								=			rsent.split(""" """   )
    __magic_name__								=			[]
    __magic_name__								=			[]
    __magic_name__								=			[]
    ragramslist.append(A_   )
    for i in range(0,	len(A_   ) - 1   ):
      if i < len(A_   ) - 1:
        __magic_name__								=			ragrams[i] + """ """ + ragrams[i + 1]
        ragrams.append(A_   )
      if i < len(A_   ) - 2:
        __magic_name__								=			ragrams[i] + """ """ + ragrams[i + 1] + """ """ + ragrams[i + 2]
        ragrams.append(A_   )
      if i < len(A_   ) - 3:
        __magic_name__								=			ragrams[i] + """ """ + ragrams[i + 1] + """ """ + ragrams[i + 2] + """ """ + ragrams[i + 3]
        ragrams.append(A_   )
    ragramslist.append(A_   )
    ragramslist.append(A_   )
    ragramslist.append(A_   )

  for i in range(0,	len(A_   ) - 1   ):
    if i < len(A_   ) - 1:
      __magic_name__								=			sagrams[i] + """ """ + sagrams[i + 1]
      sagrams.append(A_   )
    if i < len(A_   ) - 2:
      __magic_name__								=			sagrams[i] + """ """ + sagrams[i + 1] + """ """ + sagrams[i + 2]
      sagrams.append(A_   )
    if i < len(A_   ) - 3:
      __magic_name__								=			sagrams[i] + """ """ + sagrams[i + 1] + """ """ + sagrams[i + 2] + """ """ + sagrams[i + 3]
      sagrams.append(A_   )

  for i in range(0,	len(A_   ) - 1   ):
    if i < len(A_   ) - 1:
      __magic_name__								=			cagrams[i] + """ """ + cagrams[i + 1]
      cagrams.append(A_   )
    if i < len(A_   ) - 2:
      __magic_name__								=			cagrams[i] + """ """ + cagrams[i + 1] + """ """ + cagrams[i + 2]
      cagrams.append(A_   )
    if i < len(A_   ) - 3:
      __magic_name__								=			cagrams[i] + """ """ + cagrams[i + 1] + """ """ + cagrams[i + 2] + """ """ + cagrams[i + 3]
      cagrams.append(A_   )

  ((__magic_name__)	,       (__magic_name__)	,       (__magic_name__))								=			SARIngram(A_,	A_,	A_,	A_   )
  ((__magic_name__)	,       (__magic_name__)	,       (__magic_name__))								=			SARIngram(A_,	A_,	A_,	A_   )
  ((__magic_name__)	,       (__magic_name__)	,       (__magic_name__))								=			SARIngram(A_,	A_,	A_,	A_   )
  ((__magic_name__)	,       (__magic_name__)	,       (__magic_name__))								=			SARIngram(A_,	A_,	A_,	A_   )
  __magic_name__								=			sum([keepascore, keepascore, keepascore, keepascore]   ) / 4
  __magic_name__								=			sum([delascore, delascore, delascore, delascore]   ) / 4
  __magic_name__								=			sum([addascore, addascore, addascore, addascore]   ) / 4
  __magic_name__								=			(avgkeepscore + avgdelscore + avgaddscore) / 3
  return finalscore

def a__       (			A_,	A_ = True,	A_ = "13a",	A_ = True   ):






  '''simple docstring'''





  if lowercase:
    __magic_name__								=			sentence.lower()

  if tokenizer in ["13a", "intl"]:
    if version.parse(sacrebleu.__version__   ).major >= 2:
      __magic_name__								=			sacrebleu.metrics.bleu._get_tokenizer(A_   )()(A_   )
    else:
      __magic_name__								=			sacrebleu.TOKENIZERS[tokenizer]()(A_   )
  elif tokenizer == "moses":
    __magic_name__								=			sacremoses.MosesTokenizer().tokenize(A_,	return_str=A_,	escape=A_   )
  elif tokenizer == "penn":
    __magic_name__								=			sacremoses.MosesTokenizer().penn_tokenize(A_,	return_str=A_   )
  else:
    __magic_name__								=			sentence

  if not return_str:
    __magic_name__								=			normalized_sent.split()

  return normalized_sent

def a__       (			A_,	A_,	A_   ):






  '''simple docstring'''





  if not (len(A_   ) == len(A_   ) == len(A_   )):
    raise ValueError("""Sources length must match predictions and references lengths."""   )
  __magic_name__								=			0
  for src, pred, refs in zip(A_,	A_,	A_   ):
    sari_score += SARIsent(normalize(A_   ),	normalize(A_   ),	[normalize(A_   ) for sent in refs]   )
  __magic_name__								=			sari_score / len(A_   )
  return 100 * sari_score

def a__       (			A_,	A_,	A_="exp",	A_=None,	A_=False,	A_=False,	A_=False,	):






  '''simple docstring'''





  __magic_name__								=			len(references[0]   )
  if any(len(A_   ) != references_per_prediction for refs in references   ):
    raise ValueError("""Sacrebleu requires the same number of references for each prediction"""   )
  __magic_name__								=			[[refs[i] for refs in references] for i in range(A_   )]
  __magic_name__								=			sacrebleu.corpus_bleu(
      A_,	A_,	smooth_method=A_,	smooth_value=A_,	force=A_,	lowercase=A_,	use_effective_order=A_,	)
  return output.score





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION    ,			_KWARGS_DESCRIPTION	)
class 			UpperCAmelCase_     (					datasets.Metric	):


  '''simple docstring'''







  def 				_lowercase				(					self     :					Dict       )     ->      int:







    """simple docstring"""






    return datasets.MetricInfo(
        description=_DESCRIPTION     ,				citation=_CITATION     ,				inputs_description=_KWARGS_DESCRIPTION     ,				features=datasets.Features(
            {
                """predictions""": datasets.Value("""string"""     ,				id="""sequence"""       ),
                """references""": datasets.Sequence(datasets.Value("""string"""     ,				id="""sequence"""       )     ,				id="""references"""       ),
            }       )     ,				codebase_urls=[
            """https://github.com/huggingface/transformers/blob/master/src/transformers/data/metrics/squad_metrics.py""",
            """https://github.com/cocoxu/simplification/blob/master/SARI.py""",
            """https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py""",
            """https://github.com/mjpost/sacreBLEU""",
        ]     ,				reference_urls=[
            """https://www.aclweb.org/anthology/Q16-1029.pdf""",
            """https://github.com/mjpost/sacreBLEU""",
            """https://en.wikipedia.org/wiki/BLEU""",
            """https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213""",
        ]     ,				)





  def 				_lowercase				(					self     :					str     ,				UpperCamelCase__     :					Any     ,				UpperCamelCase__     :					Union[str, Any]     ,				UpperCamelCase__     :					str       )     ->      Optional[int]:







    """simple docstring"""






    __magic_name__								=			{}
    result.update({"""sari""": compute_sari(sources=UpperCamelCase__     ,				predictions=UpperCamelCase__     ,				references=UpperCamelCase__       )}       )
    result.update({"""sacrebleu""": compute_sacrebleu(predictions=UpperCamelCase__     ,				references=UpperCamelCase__       )}       )
    result.update({"""exact""": compute_em(predictions=UpperCamelCase__     ,				references=UpperCamelCase__       )}       )
    return result



import argparse
import collections
import os
import re

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
__lowerCAmelCase     :	Dict       			=  'src/transformers'
__lowerCAmelCase     :	List[Any]       			=  'docs/source/en'
__lowerCAmelCase     :	str       			=  '.'

def a__       (			A_,	A_,	A_   ):






  '''simple docstring'''





  with open(A_,	"""r""",	encoding="""utf-8""",	newline="""\n"""   ) as f:
    __magic_name__								=			f.readlines()
  # Find the start prompt.
  __magic_name__								=			0
  while not lines[start_index].startswith(A_   ):
    start_index += 1
  start_index += 1

  __magic_name__								=			start_index
  while not lines[end_index].startswith(A_   ):
    end_index += 1
  end_index -= 1

  while len(lines[start_index]   ) <= 1:
    start_index += 1
  while len(lines[end_index]   ) <= 1:
    end_index -= 1
  end_index += 1
  return "".join(lines[start_index:end_index]   ), start_index, end_index, lines


# Add here suffixes that are used to identify models, separated by |
__lowerCAmelCase     :	str       			=  'Model|Encoder|Decoder|ForConditionalGeneration'
# Regexes that match TF/Flax/PT model names.
__lowerCAmelCase     :	List[str]       			=  re.compile(R'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
__lowerCAmelCase     :	Any       			=  re.compile(R'Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
__lowerCAmelCase     :	Optional[int]       			=  re.compile(R'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')


# This is to make sure the transformers module imported is the one in the repo.
__lowerCAmelCase     :	Union[str, Any]       			=  direct_transformers_import(TRANSFORMERS_PATH)

def a__       (			A_   ):






  '''simple docstring'''





  __magic_name__								=			re.finditer(""".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)""",	A_   )
  return [m.group(0   ) for m in matches]

def a__       (			A_,	A_   ):






  '''simple docstring'''





  __magic_name__								=			2 if text == """✅""" or text == """❌""" else len(A_   )
  __magic_name__								=			(width - text_length) // 2
  __magic_name__								=			width - text_length - left_indent
  return " " * left_indent + text + " " * right_indent

def a__       (			):






  '''simple docstring'''





  __magic_name__								=			transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
  __magic_name__								=			{
      name: config_maping_names[code]
      for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
      if code in config_maping_names
  }
  __magic_name__								=			{name: config.replace("""Config""",	""""""   ) for name, config in model_name_to_config.items()}

  # Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax.
  __magic_name__								=			collections.defaultdict(A_   )
  __magic_name__								=			collections.defaultdict(A_   )
  __magic_name__								=			collections.defaultdict(A_   )
  __magic_name__								=			collections.defaultdict(A_   )
  __magic_name__								=			collections.defaultdict(A_   )

  # Let's lookup through all transformers object (once).
  for attr_name in dir(A_   ):
    __magic_name__								=			None
    if attr_name.endswith("""Tokenizer"""   ):
      __magic_name__								=			slow_tokenizers
      __magic_name__								=			attr_name[:-9]
    elif attr_name.endswith("""TokenizerFast"""   ):
      __magic_name__								=			fast_tokenizers
      __magic_name__								=			attr_name[:-13]
    elif _re_tf_models.match(A_   ) is not None:
      __magic_name__								=			tf_models
      __magic_name__								=			_re_tf_models.match(A_   ).groups()[0]
    elif _re_flax_models.match(A_   ) is not None:
      __magic_name__								=			flax_models
      __magic_name__								=			_re_flax_models.match(A_   ).groups()[0]
    elif _re_pt_models.match(A_   ) is not None:
      __magic_name__								=			pt_models
      __magic_name__								=			_re_pt_models.match(A_   ).groups()[0]

    if lookup_dict is not None:
      while len(A_   ) > 0:
        if attr_name in model_name_to_prefix.values():
          __magic_name__								=			True
          break
        # Try again after removing the last word in the name
        __magic_name__								=			"""""".join(camel_case_split(A_   )[:-1]   )

    # Let's build that table!
  __magic_name__								=			list(model_name_to_config.keys()   )
  model_names.sort(key=str.lower   )
  __magic_name__								=			["""Model""", """Tokenizer slow""", """Tokenizer fast""", """PyTorch support""", """TensorFlow support""", """Flax Support"""]
  # We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).
  __magic_name__								=			[len(A_   ) + 2 for c in columns]
  __magic_name__								=			max([len(A_   ) for name in model_names]   ) + 2

  # Build the table per se
  __magic_name__								=			"""|""" + """|""".join([_center_text(A_,	A_   ) for c, w in zip(A_,	A_   )]   ) + """|\n"""
  # Use ":-----:" format to center-aligned table cell texts
  table += "|" + "|".join([""":""" + """-""" * (w - 2) + """:""" for w in widths]   ) + "|\n"

  __magic_name__								=			{True: """✅""", False: """❌"""}
  for name in model_names:
    __magic_name__								=			model_name_to_prefix[name]
    __magic_name__								=			[
        name,
        check[slow_tokenizers[prefix]],
        check[fast_tokenizers[prefix]],
        check[pt_models[prefix]],
        check[tf_models[prefix]],
        check[flax_models[prefix]],
    ]
    table += "|" + "|".join([_center_text(A_,	A_   ) for l, w in zip(A_,	A_   )]   ) + "|\n"
  return table

def a__       (			A_=False   ):






  '''simple docstring'''





  __magic_name__	,       __magic_name__	,       __magic_name__	,       __magic_name__								=			_find_text_in_file(
      filename=os.path.join(A_,	"""index.md"""   ),	start_prompt="""<!--This table is updated automatically from the auto modules""",	end_prompt="""<!-- End table-->""",	)
  __magic_name__								=			get_model_table_from_auto_modules()

  if current_table != new_table:
    if overwrite:
      with open(os.path.join(A_,	"""index.md"""   ),	"""w""",	encoding="""utf-8""",	newline="""\n"""   ) as f:
        f.writelines(lines[:start_index] + [new_table] + lines[end_index:]   )
    else:
      raise ValueError(
          """The model table in the `index.md` has not been updated. Run `make fix-copies` to fix this."""   )


if __name__ == "__main__":
    __lowerCAmelCase     :	Tuple       			=  argparse.ArgumentParser()
    parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
    __lowerCAmelCase     :	Optional[Any]       			=  parser.parse_args()

    check_model_table(args.fix_and_overwrite)

